Day_32_Discretization.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_32_Discretization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "GmDCSZIx6NgV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n"
      ],
      "metadata": {
        "id": "duQQqPKc9rNW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/Manish-k723/100-days-of-ML/main/train.csv', usecols = ['Age','Fare', 'Survived'])"
      ],
      "metadata": {
        "id": "6fVY6bIc-b8m"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Slun8EkH-9xz",
        "outputId": "6aea8996-85e7-40ab-c20c-609563eb61c9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    int64  \n",
            " 1   Age       714 non-null    float64\n",
            " 2   Fare      891 non-null    float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 21.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace =True)"
      ],
      "metadata": {
        "id": "es47f09P-_Te"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        "id": "Luad8Cl__IWr",
        "outputId": "23103fbe-d21f-434b-960a-da057f761861"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 714 entries, 0 to 890\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  714 non-null    int64  \n",
            " 1   Age       714 non-null    float64\n",
            " 2   Fare      714 non-null    float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 22.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:,1:]\n",
        "y = df.iloc[:,0]"
      ],
      "metadata": {
        "id": "8jpzvQuK_MBb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "jC0vrej__ToQ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "zIVa_zLF_kZX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(x_train, y_train)\n",
        "y_pred = clf.predict(x_test)\n",
        "\n",
        "accuracy_score(y_pred, y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFUz_DhY_zT3",
        "outputId": "654e875d-7334-4d96-9772-ac6891e97e1c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63.63636363636363"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cross_val_score(clf, x, y, cv = 10, scoring = 'accuracy'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgKDXOtAAEwF",
        "outputId": "2e7a9b83-03a9-47b9-81a7-30c93b56c2ae"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6289319248826292"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, going to apply binning"
      ],
      "metadata": {
        "id": "AxyoeMhfCPQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kbin_age = KBinsDiscretizer(n_bins = 5, strategy = 'kmeans', encode = 'ordinal')\n",
        "kbin_fare = KBinsDiscretizer(n_bins = 5, strategy = 'kmeans', encode = 'ordinal')\n",
        "\n",
        "trf = ColumnTransformer([\n",
        "    ('first', kbin_age,[0]),\n",
        "    ('second', kbin_fare,[1])])"
      ],
      "metadata": {
        "id": "TeYxJSz9Byvv"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trf.named_transformers_['first'].bin_edges_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NU10z84dEdSY",
        "outputId": "c1613e0f-84a2-4efe-a829-8a2a1dd9becb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([array([ 1.        , 13.54505632, 25.49492263, 36.14664502, 48.33333333,\n",
              "              62.        ])                                                   ],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_trans = trf.fit_transform(x_train)\n",
        "x_test_trans = trf.transform(x_test)\n",
        "\n",
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "clf.fit(x_train_trans, y_train)\n",
        "y_pred = clf.predict(x_test_trans)\n",
        "\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4mD7dUNClhy",
        "outputId": "b0323fe7-2300-4409-a0e3-6d25cfc33a15"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6223776223776224"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No improvement"
      ],
      "metadata": {
        "id": "7eX9bKcVGzyV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = pd.DataFrame({\n",
        "    'age':x_train['Age'],\n",
        "    'age_trf':x_train_trans[:,0],\n",
        "    'fare':x_train['Fare'],\n",
        "    'fare_trf':x_train_trans[:,1]\n",
        "})\n",
        "\n",
        "output['age_labels'] = pd.cut(x=x_train['Age'],\n",
        "                                    bins=trf.named_transformers_['first'].bin_edges_[0].tolist())\n",
        "output['fare_labels'] = pd.cut(x=x_train['Fare'],\n",
        "                                    bins=trf.named_transformers_['second'].bin_edges_[0].tolist())"
      ],
      "metadata": {
        "id": "yz7jDUxpFqUP"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZA50FEf0FteS",
        "outputId": "93b1dbe6-383e-4e04-9cf4-fbac152d835c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  age_trf      fare  fare_trf        age_labels        fare_labels\n",
              "328  31.0      2.0   20.5250       0.0  (27.028, 39.354]      (0.0, 42.424]\n",
              "73   26.0      1.0   14.4542       0.0  (12.696, 27.028]      (0.0, 42.424]\n",
              "253  30.0      2.0   16.1000       0.0  (27.028, 39.354]      (0.0, 42.424]\n",
              "719  33.0      2.0    7.7750       0.0  (27.028, 39.354]      (0.0, 42.424]\n",
              "666  25.0      1.0   13.0000       0.0  (12.696, 27.028]      (0.0, 42.424]\n",
              "..    ...      ...       ...       ...               ...                ...\n",
              "92   46.0      3.0   61.1750       1.0  (39.354, 54.215]  (42.424, 100.624]\n",
              "134  25.0      1.0   13.0000       0.0  (12.696, 27.028]      (0.0, 42.424]\n",
              "337  41.0      3.0  134.5000       2.0  (39.354, 54.215]   (100.624, 186.5]\n",
              "548  33.0      2.0   20.5250       0.0  (27.028, 39.354]      (0.0, 42.424]\n",
              "130  33.0      2.0    7.8958       0.0  (27.028, 39.354]      (0.0, 42.424]\n",
              "\n",
              "[571 rows x 6 columns]"
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>328</th>\n",
              "      <td>31.0</td>\n",
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              "      <td>20.5250</td>\n",
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              "      <td>(27.028, 39.354]</td>\n",
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              "      <th>73</th>\n",
              "      <td>26.0</td>\n",
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              "      <th>719</th>\n",
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              "      <th>92</th>\n",
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              "      <td>61.1750</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(39.354, 54.215]</td>\n",
              "      <td>(42.424, 100.624]</td>\n",
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              "      <th>134</th>\n",
              "      <td>25.0</td>\n",
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              "      <td>13.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>(12.696, 27.028]</td>\n",
              "      <td>(0.0, 42.424]</td>\n",
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              "      <th>337</th>\n",
              "      <td>41.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>134.5000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>(39.354, 54.215]</td>\n",
              "      <td>(100.624, 186.5]</td>\n",
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              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>33.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>20.5250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>(27.028, 39.354]</td>\n",
              "      <td>(0.0, 42.424]</td>\n",
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              "      <th>130</th>\n",
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              "      <td>2.0</td>\n",
              "      <td>7.8958</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "kbin_age1 = KBinsDiscretizer(n_bins = 15, strategy = 'quantile', encode = 'ordinal')\n",
        "kbin_fare1 = KBinsDiscretizer(n_bins = 15, strategy = 'quantile', encode = 'ordinal')\n",
        "\n",
        "trf1 = ColumnTransformer([\n",
        "    ('first', kbin_age1,[0]),\n",
        "    ('second', kbin_fare1,[1])])\n",
        "\n",
        "x_train_trans1 = trf1.fit_transform(x_train)\n",
        "x_test_trans1 = trf1.transform(x_test)\n",
        "\n",
        "clf1 = DecisionTreeClassifier()\n",
        "\n",
        "clf1.fit(x_train_trans1, y_train)\n",
        "accuracy_score(y_test,clf1.predict(x_test_trans1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nPYqjb9DCPI",
        "outputId": "4fae6b02-39e4-4696-8ef3-6967129a0428"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6363636363636364"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kbin_age2 = KBinsDiscretizer(n_bins = 10, strategy = 'uniform', encode = 'ordinal')\n",
        "kbin_fare2 = KBinsDiscretizer(n_bins = 10\n",
        "                              , strategy = 'uniform', encode = 'ordinal')\n",
        "\n",
        "trf2 = ColumnTransformer([\n",
        "    ('first', kbin_age2,[0]),\n",
        "    ('second', kbin_fare2,[1])])\n",
        "\n",
        "x_train_trans2 = trf2.fit_transform(x_train)\n",
        "x_test_trans2 = trf2.transform(x_test)\n",
        "\n",
        "clf2 = DecisionTreeClassifier()\n",
        "\n",
        "clf2.fit(x_train_trans2, y_train)\n",
        "accuracy_score(y_test,clf2.predict(x_test_trans2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BFjv0drHp9D",
        "outputId": "303cefab-994e-41b8-fa60-e764e864c4ff"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6783216783216783"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def discretize(bins,strategy):\n",
        "    kbin_age = KBinsDiscretizer(n_bins=bins,encode='ordinal',strategy=strategy)\n",
        "    kbin_fare = KBinsDiscretizer(n_bins=bins,encode='ordinal',strategy=strategy)\n",
        "    \n",
        "    trf = ColumnTransformer([\n",
        "        ('first',kbin_age,[0]),\n",
        "        ('second',kbin_fare,[1])\n",
        "    ])\n",
        "    \n",
        "    X_trf = trf.fit_transform(x)\n",
        "    print('Cross Val Score is {}'.format(np.mean(cross_val_score(DecisionTreeClassifier(),x,y,cv=10,scoring='accuracy'))))\n",
        "    \n",
        "    plt.figure(figsize=(14,4))\n",
        "    plt.subplot(121)\n",
        "    plt.hist(x['Age'])\n",
        "    plt.title(\"Age Before\")\n",
        "\n",
        "    plt.subplot(122)\n",
        "    plt.hist(X_trf[:,0],color='red')\n",
        "    plt.title(\"Age After\")\n",
        "\n",
        "    plt.show()\n",
        "    \n",
        "    plt.figure(figsize=(14,4))\n",
        "    plt.subplot(121)\n",
        "    plt.hist(x['Fare'])\n",
        "    plt.title(\"Fare Before\")\n",
        "\n",
        "    plt.subplot(122)\n",
        "    plt.hist(X_trf[:,1],color='red')\n",
        "    plt.title(\"Fare After\")\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "BExpZPWrQEeE"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discretize(10,'quantile')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "-5Pk0lIaQF1Y",
        "outputId": "3a12608b-da71-4800-eba0-cfa7a4ac7ee7"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross Val Score is 0.6330985915492957\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The change is very clear. In each bin, there is almost equal number of items. This is what quantile binner do."
      ],
      "metadata": {
        "id": "p8kqTRSkRDXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "discretize(10,'kmeans')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "Qcl4WnE4RCza",
        "outputId": "ff0cdde5-3051-4e8f-e0ad-57834c38f971"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6373435054773082\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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IdQAAAIIsJSVFq1at0oYNG5SXl6cDBw7o+uuv1/Hjx+V2uxUeHq7o6Gi/98THx8vtdp91nbm5uXI4HOaSmJgY4L0AAADNjcuvAAAAgmzUqFHm3wMHDlRKSop69OihV199VR06dDivdebk5GjatGnma6/XS7ADAEArw5k6AAAALUx0dLQuu+wy7d+/X06nU7W1taqqqvIbU1FR0eA9eE6LiIiQ3W73WwAAQOtCqAMAANDCnDhxQl9++aW6du2qwYMHq127dtq8ebPZX1ZWpvLycrlcriBWiWZns1lvAQAEFJdfAQAABNkjjzyi0aNHq0ePHjp8+LDmzJmj0NBQ3X333XI4HBo/frymTZummJgY2e12Pfjgg3K5XDz5CgCANo5QBwAAIMj+/ve/6+6779bXX3+t2NhYXXfdddq+fbtiY2MlSYsXL1ZISIgyMjLk8/mUlpam5cuXB7lqAAAQbIQ6AAAAQbZ69ep/2d++fXstW7ZMy5Yta6aKAACAFXBPHQAAAAAAAAsi1AEAAAAAALAgQh0AAAAAAAALItQBAAAAAACwIEIdAAAAAAAAC+LpVwAAAABwms0W7AoazzCCXQGAIOFMHQAAAAAAAAsi1AEAAAAAALAgQh0AAAAAAAALItQBAAAAAACwIEIdAAAAAAAACyLUAQAAAAAAsCBCHQAAAAAAAAsi1AEAAAAAALCgsGAXAAAAAKCVstmCXQEAtGqcqQMAAAAAAGBBnKkDAAAAAFZmtTOiDCPYFQCtBqEOguKSmW8Gu4RGO7gwPdglAAAAAABg4vIrAAAAAAAACyLUAQAAAAAAsCBCHQAAAAAAAAsi1AEAAAAAALAgQh0AAAAAAAALItQBAAAAAACwIB5pDgAAAABoPjZbsCtoPMMIdgVAgwh1AAAAAABobQjP2gRCHQAAAAAA/hUrBiRoE4Ia6ixbtkxPP/203G63Bg0apOeee07XXHNNMEsCWpVLZr4Z7BIa7eDC9GCXAAAtFnMnAADwz4IW6vzhD3/QtGnTtGLFCqWkpGjJkiVKS0tTWVmZ4uLiglUWgCAjiAKAhjF3AgAA3xe0p18988wzmjBhgn72s58pOTlZK1asUGRkpH77298GqyQAAIAWi7kTAKDVs9mstwRZUM7Uqa2tVWlpqXJycsy2kJAQpaamqqSk5IzxPp9PPp/PfO3xeCRJXq83IPXV+74JyHphbd2nrgl2CWihAvW/RbC2/nM2BruERvtsXlqTr/P0fx8GNz68II2dO0nNP38CAKBNCsD3amPmT0EJdf7xj3+orq5O8fHxfu3x8fH64osvzhifm5urefPmndGemJgYsBoB4Fw5lgS7AqBpBPLf8vHjx+VwOAK3gVausXMnifkTAADNIoDzm3OZP1ni6Vc5OTmaNm2a+bq+vl7Hjh1T586dZWvi0528Xq8SExN16NAh2e32Jl13W8exDSyOb+BwbAOL4xs4LeHYGoah48ePKyEhISjbb8uaa/7UEv6d4f/webQsfB4tB59Fy8Ln8a81Zv4UlFCnS5cuCg0NVUVFhV97RUWFnE7nGeMjIiIUERHh1xYdHR3IEmW32/nHFSAc28Di+AYOxzawOL6BE+xjyxk6F66xcyep+edPwf53Bn98Hi0Ln0fLwWfRsvB5nN25zp+CcqPk8PBwDR48WJs3bzbb6uvrtXnzZrlcrmCUBAAA0GIxdwIAAA0J2uVX06ZNU1ZWloYMGaJrrrlGS5YsUXV1tX72s58FqyQAAIAWi7kTAAD4vqCFOj/5yU909OhRzZ49W263W1dccYU2bNhwxg0Am1tERITmzJlzxunKuHAc28Di+AYOxzawOL6Bw7FtXZg74VzwebQsfB4tB59Fy8Ln0XRsBs8YBQAAAAAAsJyg3FMHAAAAAAAAF4ZQBwAAAAAAwIIIdQAAAAAAACyIUAcAAAAAAMCCCHX+ybJly3TJJZeoffv2SklJ0Ycffhjsklq8d999V6NHj1ZCQoJsNptef/11v37DMDR79mx17dpVHTp0UGpqqvbt2+c35tixYxo7dqzsdruio6M1fvx4nThxohn3omXKzc3V1VdfrU6dOikuLk5jxoxRWVmZ35iamhplZ2erc+fO6tixozIyMlRRUeE3pry8XOnp6YqMjFRcXJymT5+uU6dONeeutEh5eXkaOHCg7Ha77Ha7XC6X1q9fb/ZzbJvOwoULZbPZNGXKFLON43v+5s6dK5vN5rckJSWZ/RxbNDfmTy3DucwbEBwNfQ+ieX311Ve655571LlzZ3Xo0EEDBgzQRx99FOyy2py6ujo99thj6tmzpzp06KBLL71Ujz/+uHh204Uh1Pn//vCHP2jatGmaM2eOPv74Yw0aNEhpaWmqrKwMdmktWnV1tQYNGqRly5Y12L9o0SItXbpUK1as0I4dOxQVFaW0tDTV1NSYY8aOHas9e/aoqKhIhYWFevfddzVx4sTm2oUWq7i4WNnZ2dq+fbuKiop08uRJjRgxQtXV1eaYqVOnat26dVqzZo2Ki4t1+PBh3X777WZ/XV2d0tPTVVtbq23btumll17SqlWrNHv27GDsUovSrVs3LVy4UKWlpfroo49000036bbbbtOePXskcWybys6dO/Vf//VfGjhwoF87x/fCXH755Tpy5Ii5vP/++2YfxxbNiflTy3Eu8wY0v7N9D6L5/O///q+GDRumdu3aaf369dq7d6/+8z//UxdddFGwS2tznnrqKeXl5ek3v/mNPv/8cz311FNatGiRnnvuuWCXZm0GDMMwjGuuucbIzs42X9fV1RkJCQlGbm5uEKuyFknG2rVrzdf19fWG0+k0nn76abOtqqrKiIiIMH7/+98bhmEYe/fuNSQZO3fuNMesX7/esNlsxldffdVstVtBZWWlIckoLi42DOO7Y9muXTtjzZo15pjPP//ckGSUlJQYhmEYb731lhESEmK43W5zTF5enmG32w2fz9e8O2ABF110kfHf//3fHNsmcvz4caNPnz5GUVGR8W//9m/GQw89ZBgG/3Yv1Jw5c4xBgwY12MexRXNj/tRyfX/egOZ3tu9BNK8ZM2YY1113XbDLgGEY6enpxs9//nO/tttvv90YO3ZskCpqHThTR1Jtba1KS0uVmppqtoWEhCg1NVUlJSVBrMzaDhw4ILfb7XdcHQ6HUlJSzONaUlKi6OhoDRkyxByTmpqqkJAQ7dixo9lrbsk8Ho8kKSYmRpJUWlqqkydP+h3fpKQkde/e3e/4DhgwQPHx8eaYtLQ0eb1e84wUfHfmwurVq1VdXS2Xy8WxbSLZ2dlKT0/3O44S/3abwr59+5SQkKBevXpp7NixKi8vl8SxRfNi/tSyfX/egOZ3tu9BNK8//elPGjJkiO68807FxcXpyiuv1AsvvBDsstqka6+9Vps3b9Zf/vIXSdKf//xnvf/++xo1alSQK7O2sGAX0BL84x//UF1dnd8EV5Li4+P1xRdfBKkq63O73ZLU4HE93ed2uxUXF+fXHxYWppiYGHMMpPr6ek2ZMkXDhg1T//79JX137MLDwxUdHe039vvHt6Hjf7qvrdu9e7dcLpdqamrUsWNHrV27VsnJydq1axfH9gKtXr1aH3/8sXbu3HlGH/92L0xKSopWrVqlvn376siRI5o3b56uv/56ffbZZxxbNCvmTy1XQ/MGNK9/9T2I5vXXv/5VeXl5mjZtmn75y19q586d+sUvfqHw8HBlZWUFu7w2ZebMmfJ6vUpKSlJoaKjq6ur05JNPauzYscEuzdIIdQALyM7O1meffeZ33wxcuL59+2rXrl3yeDz64x//qKysLBUXFwe7LMs7dOiQHnroIRUVFal9+/bBLqfV+edfswYOHKiUlBT16NFDr776qjp06BDEygC0FMwbgovvwZalvr5eQ4YM0YIFCyRJV155pT777DOtWLGCUKeZvfrqq8rPz1dBQYEuv/xy7dq1S1OmTFFCQgKfxQXg8itJXbp0UWho6BlPB6moqJDT6QxSVdZ3+tj9q+PqdDrPuJniqVOndOzYMY79/zd58mQVFhZq69at6tatm9nudDpVW1urqqoqv/HfP74NHf/TfW1deHi4evfurcGDBys3N1eDBg3Ss88+y7G9QKWlpaqsrNRVV12lsLAwhYWFqbi4WEuXLlVYWJji4+M5vk0oOjpal112mfbv38+/XTQr5k8t09nmDWg+P/Q9WFdXF+wS25SuXbsqOTnZr61fv37mpctoPtOnT9fMmTOVmZmpAQMGaNy4cZo6dapyc3ODXZqlEerou/9jN3jwYG3evNlsq6+v1+bNm+VyuYJYmbX17NlTTqfT77h6vV7t2LHDPK4ul0tVVVUqLS01x2zZskX19fVKSUlp9ppbEsMwNHnyZK1du1ZbtmxRz549/foHDx6sdu3a+R3fsrIylZeX+x3f3bt3+wVnRUVFstvtZ3y54bv/7n0+H8f2Ag0fPly7d+/Wrl27zGXIkCEaO3as+TfHt+mcOHFCX375pbp27cq/XTQr5k8tyw/NG9B8fuh7MDQ0NNgltinDhg1TWVmZX9tf/vIX9ejRI0gVtV3ffPONQkL8I4jQ0FDV19cHqaJWIth3am4pVq9ebURERBirVq0y9u7da0ycONGIjo72ezoIznT8+HHjk08+MT755BNDkvHMM88Yn3zyifG3v/3NMAzDWLhwoREdHW288cYbxqeffmrcdtttRs+ePY1vv/3WXMfIkSONK6+80tixY4fx/vvvG3369DHuvvvuYO1SizFp0iTD4XAY77zzjnHkyBFz+eabb8wx999/v9G9e3djy5YtxkcffWS4XC7D5XKZ/adOnTL69+9vjBgxwti1a5exYcMGIzY21sjJyQnGLrUoM2fONIqLi40DBw4Yn376qTFz5kzDZrMZb7/9tmEYHNum9v2nfnB8z9/DDz9svPPOO8aBAweMDz74wEhNTTW6dOliVFZWGobBsUXzYv7UcpzLvAHBw9OvgufDDz80wsLCjCeffNLYt2+fkZ+fb0RGRhq/+93vgl1am5OVlWVcfPHFRmFhoXHgwAHjtddeM7p06WI8+uijwS7N0gh1/slzzz1ndO/e3QgPDzeuueYaY/v27cEuqcXbunWrIemMJSsryzCM7x5r/thjjxnx8fFGRESEMXz4cKOsrMxvHV9//bVx9913Gx07djTsdrvxs5/9zDh+/HgQ9qZlaei4SjJWrlxpjvn222+NBx54wLjooouMyMhI49///d+NI0eO+K3n4MGDxqhRo4wOHToYXbp0MR5++GHj5MmTzbw3Lc/Pf/5zo0ePHkZ4eLgRGxtrDB8+3Ax0DINj29S+P5nl+J6/n/zkJ0bXrl2N8PBw4+KLLzZ+8pOfGPv37zf7ObZobsyfWoZzmTcgeAh1gmvdunVG//79jYiICCMpKcl4/vnng11Sm+T1eo2HHnrI6N69u9G+fXujV69exq9+9SvD5/MFuzRLsxmGYTT32UEAAAAAAAC4MNxTBwAAAAAAwIIIdQAAAAAAACyIUAcAAAAAAMCCCHUAAAAAAAAsiFAHAAAAAADAggh1AAAAAAAALIhQBwAAAAAAwIIIdQAAAAAAACyIUAcAAAAAAMCCCHUAAAAAAAAsiFAHAAAAAADAggh1AAAAAAAALOj/AWk1aFc1rrIjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No much changes."
      ],
      "metadata": {
        "id": "Cs9qJSdyRVsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "discretize(10,'uniform')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "x48F7LdxQOsx",
        "outputId": "69dc8494-9835-4927-dd5d-aeef9d610361"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6359350547730829\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No changes."
      ],
      "metadata": {
        "id": "jcBKM0R_RYcE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Binarization**"
      ],
      "metadata": {
        "id": "i4RspzQySzat"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "# It can be used for cases like yes or no sometimes."
      ],
      "metadata": {
        "id": "ix5CupGtS1z5"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# trf = ColumnTransformer([\n",
        "#     ('bin',Binarizer(copy=False),['family'])\n",
        "# ],remainder='passthrough')"
      ],
      "metadata": {
        "id": "tdS6ZKtmTdik"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}